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Claim this profileWorks317 from public data
- Visual saliency based on multiscale deep features1,361
This paper discovers that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
- 920
- Neural Style Transfer: A Review895
A taxonomy of current algorithms in the field of NST is proposed and several evaluation methods are presented and compared to compare different NST algorithms both qualitatively and quantitatively.
- nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer812
nFormer is introduced, a 3D transformer for volumetric medical image segmentation that not only exploits the combination of interleaved convolution and self-attention operations, but also introduces local and global volume-based self-Attention mechanism to learn volume representations.
- Deep Contrast Learning for Salient Object Detection774
This paper proposes an end-to-end deep contrast network that significantly improves the state of the art in salient object detection and extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries.
- Mesh editing with poisson-based gradient field manipulation682
A novel approach to mesh editing with the Poisson equation as the theoretical foundation that modifies the original mesh geometry implicitly through gradient field manipulation, and can outperform previous related mesh editing techniques.
- Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping621
This paper presents how the image-based rendering technique of view-dependent texture-mapping (VDTM), a technique for generating novel views of a scene with approximately known geometry making maximal use of a sparse set of original views, can be efficiently implemented using projective texture mapping, a feature commonly available in polygon graphics hardware.
- Visual Saliency Detection Based on Multiscale Deep CNN Features394
This paper discovers that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
- Visual Saliency Detection Based on Multiscale367
This paper discovers that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
- A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics349
The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary disease and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively).
- Adaptive Context Selection for Polyp Segmentation344
This work proposes an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM), which shows outstanding performance compared with other state-of-the-art methods.
- 337
- High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference331
This work proposes a data-driven method for recovering missing parts of 3D shapes based on a new deep learning architecture consisting of a global structure inference network and a local geometry refinement network that outperforms existing state-of-the-art work on shape completion.
- Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up317
This paper builds complementary parts models in a weakly supervised manner to retrieve information suppressed by dominant object parts detected by convolutional neural networks and builds a bi-directional long short-term memory (LSTM) network to fuze and encode the partial information of these complementary parts into a comprehensive feature for image classification.
- Automatic Photo Adjustment Using Deep Neural Networks302
This article forms automatic photo adjustment in a manner suitable for deep neural networks, and introduces an image descriptor accounting for the local semantics of an image that can model local adjustments that depend on image semantics.
- Instance-Level Salient Object Segmentation297
This paper presents a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image, and proposes a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours.
- Cross-modality deep feature learning for brain tumor segmentation292
The proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance when compared with the baseline methods and state-of-the-art methods.
- Dynamic Graph Attention for Referring Expression Comprehension263
This paper proposes a dynamic graph attention network to perform multi-step reasoning by modeling both the relationships among the objects in the image and the linguistic structure of the expression, and constructs a graph for the image with the nodes and edges corresponding to the objects and their relationships respectively.
- 256
- Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning256
This paper introduces a deep transfer learning scheme, called selective joint fine-tuning, for improving the performance of deep learning tasks with insufficient training data, and can improve the classification accuracy by 2% - 10% using a single model.
- AnL1image transform for edge-preserving smoothing and scene-level intrinsic decomposition242
An image transform based on the L1 norm for piecewise image flattening that can effectively preserve and sharpen salient edges and contours while eliminating insignificant details, producing a nearly piecewise constant image with sparse structures is introduced.
- A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images221
The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists, and the higher sensitivity of deep learning model in detectingCOVID- 19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis.
- Recovering photometric properties of architectural scenes from photographs220
The goal of generating photorealistic renderings of real architectural scenes under novel lighting conditions has been achieved and the basic approach to solving a series of optimization problems to find the parameters of appropriate lighting and reflectance models that best explain the measured values in the various photographs of the scene.
- Motion Guided Attention for Video Salient Object Detection216
A multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using twoSub-networks, one sub-network for salient object Detection in still images and the other for motion saliency detection in optical flow images, which significantly outperforms existing state-of-the-art algorithms on a wide range of benchmarks.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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